Citation

Karamshuk D, Shaw F, Brownlie J, Sastry N. Online Soc. Netw. Media 2017; 1: 33-43.

Copyright

(Copyright © 2017, Elsevier Publishing)

DOI

10.1016/j.osnem.2017.01.002

PMID

unavailable

Abstract

With the rise of social media, a vast amount of new primary research material has become available to social scientists, but the sheer volume and variety of this make it difficult to access through the traditional approaches: close reading and nuanced interpretations of manual qualitative coding and analysis. This paper sets out to bridge the gap by developing semi-automated replacements for manual coding through a mixture of crowdsourcing and machine learning, seeded by the development of a careful manual coding scheme from a small sample of data. To show the promise of this approach, we attempt to create a nuanced categorisation of responses on Twitter to several recent high profile deaths by suicide. Through these, we show that it is possible to code automatically across a large dataset to a high degree of accuracy (71%), and discuss the broader possibilities and pitfalls of using Big Data methods for Social Science. © 2017 The Authors

Language: en

Keywords

Crowd-sourcing; Crowdflower; Emotional distress; High-profile suicides; Natural language processing; Public empathy; Social media; Social science